As is known in the art, each year a relatively large number of new cancer cases are diagnosed. Every one in four deaths in the United States is from cancer. The National Cancer Institute estimates that a total of 8.2 million people in the United States have a history of cancer and are either cured or in the process of receiving therapy.
One type of cancer referred to as bronchogenic cancer initially manifests as a pulmonary nodule and is the leading cause of death from cancer. Additionally, the lung parenchyma is a frequent site for metastatic disease from extra-thoracic malignancies and primary bronchogenic cancer. Breast, renal, thyroid, testicular, and sarcomatous tumors are common malignancies that spread to the lung. Most frequently spread is manifested as nodules, although spread can manifest as lymphadenopathy and lymphangitic patterns.
Numerous patients with a primary thoracic and extra-thoracic malignancy undergo thoracic computed tomography (CT) scans to evaluate for metastasis and to evaluate for response of tumor to therapy. The manual detection, measurement of multiple pulmonary nodules, and assessment for change on sequential chest CTs can be time consuming and tedious. Currently, when a patient undergoes a thoracic CT, a radiologist first detects whether there are nodules present. Then the radiologist identifies the nodule on the image it appears the largest. The largest dimension in this plane is then measured, and the distance at an orthogonal projection is then measured. If there is a previous study, the dimensions of the nodule, if present, is measured similarly and the measurements on the two studies compared. A change in shape or volume of the nodule is noted. All these steps allow for observer error, inter-observer variation and other human sources of error. Also, prior art techniques have been developed to manually register 18-fluorodeoxyglucose PET scans of the chest with CT scans of the chest. Since such techniques are manual, however, they are time consuming and prone to human error.
Studies have demonstrated that computer-aided diagnosis systems improve a radiologist's performance (e.g. receiver operator characteristics) for detection of pulmonary nodules and interstitial lung disease on chest radiographs. Prior art systems have primarily addressed automated nodule detection on CT scans and chest radiographs.
Prior art techniques for registering CT images between scans have been investigated primarily for the brain and involving registration of positron emission tomography (PET) images with MRI or CT. Automated registration of redistribution and reinjection studies of thallium-201 cardiac single photon emission computed tomography studies have been performed in phantoms and patients. Some studies have investigated comparing sequential images within the same CT study. Still other studies describe the registration of different three-dimensional data sets of the brain to transfer preoperative surgical plans to the operating room.
There is, however, a need to better quantify the extent of pulmonary nodules and their change over time. It would thus be further desirable to provide a system that quantitates the extent of pulmonary nodules and their change over time. Furthermore, none of these prior art registration approaches, however, have compared two different CT studies of the chest to automatically assess change in nodule volume.
It would also be desirable to provide a system which automatically detects both cancerous and non-cancerous nodules from a first set of CT images taken in a single study, and which also compares the nodules detected in the first set of CT images with nodules found in one or more additional sets of CT images taken in sequential studies. It would also be desirable to automatically classify the nodules detected in the sets of CT images. It would also be desirable to provide a computer diagnosis system that automatically detects nodules, quantitates their volume, and assesses their change over time in sequential CT scans. It would be further desirable to provide a system that can automatically detect nodules in low-dose CT screening for lung cancer in higher risk patients to decrease interpretation time and false negatives in a screening scenario. It would also be desirable to provide a technique which increases the accuracy of examinations and diagnosis using CT scans. It would be further desirable to provide a system and technique which reduces the number of images an examiner (e.g. a doctor) must examine to determine if a nodule exists in a patient lung. It would be further desirable to provide a technique for automatic detection of nodules in a lung.